Molecule Generation from Input-Attributions over Graph Convolutional Networks. - Dylan Savoia; Alessio Ragno; Roberto Capobianco (CoRR, 2022)

Semi-Supervised GCN for learning Molecular Structure-Activity Relationships. - Alessio Ragno; Dylan Savoia; Roberto Capobianco (CoRR, 2022)

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal. - Eleonora Proia; Alessio Ragno; Lorenzo Antonini; Manuela Sabatino; Milan Mladenovic; Roberto Capobianco; Rino Ragno (J. Comput. Aided Mol. Des., 2022)

Understanding Deep RL Agent Decisions: a Novel Interpretable Approach with Trainable Prototypes. - Caterina Borzillo; Alessio Ragno; Roberto Capobianco (XAI.it@AI*IA, 2023)

Memory Replay For Continual Learning With Spiking Neural Networks. - Michela Proietti; Alessio Ragno; Roberto Capobianco (MLSP, 2023)

Transparent Explainable Logic Layers. - Alessio Ragno; Marc Plantevit; Céline Robardet; Roberto Capobianco (ECAI, 2024)

Identifying Candidates for Protein-Protein Interaction: A Focus on NKp46's Ligands. - Alessia Borghini; Federico Di Valerio; Alessio Ragno; Roberto Capobianco (EXPLIMED@ECAI, 2024)

Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening. - Michela Proietti; Alessio Ragno; Biagio La Rosa; Rino Ragno; Roberto Capobianco (Mach. Learn., 2024)

Prototype-Based Interpretable Graph Neural Networks. - Alessio Ragno; Biagio La Rosa; Roberto Capobianco (IEEE Trans. Artif. Intell., 2024)

IMPO: Interpretable Memory-based Prototypical Pooling. - Alessio Ragno; Roberto Capobianco (WSDM, 2025)

Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms. - Roberta Astolfi; Alessandra Oliva; Antonio Raffo; Filippo Sapienza; Alessio Ragno; Eleonora Proia; Claudio M. Mastroianni; Cristina Luceri; Mijat Bozovic; Milan Mladenovic; Rosanna Papa; Patrizia Bottoni; et al. (J. Chem. Inf. Model., 2025)

Topology-based explanations for neural networks. - Alessio Ragno (2025)